For example, in econometric time series analysis, dummy variables may be used to indicate the occurrence of wars, or major strikes. Too many dummy variables result in a model that does not provide any general conclusions.ĭummy variables are useful in various cases. They can also help us to control for confounding factors and improve the validity of our results.Īs with any addition of variables to a model, the addition of dummy variables will increases the within-sample model fit ( coefficient of determination), but at a cost of fewer degrees of freedom and loss of generality of the model (out of sample model fit). Dummy variables are useful because they allow us to include categorical variables in our analysis, which would otherwise be difficult to include due to their non-numeric nature. In this case, multiple dummy variables would be created to represent each level of the variable, and only one dummy variable would take on a value of 1 for each observation. In machine learning this is known as one-hot encoding.ĭummy variables are commonly used in regression analysis to represent categorical variables that have more than two levels, such as education level or occupation. The variable could take on a value of 1 for males and 0 for females (or vice versa). For example, if we were studying the relationship between biological sex and income, we could use a dummy variable to represent the sex of each individual in the study. In regression analysis, a dummy variable (also known as indicator variable or just dummy) is one that takes a binary value (0 or 1) to indicate the absence or presence of some categorical effect that may be expected to shift the outcome. For the usage in computing and math, see Bound variable. This article is about the usage in statistics.
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